One-factor-at-a-time method: Difference between revisions

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Despite these criticisms, some researchers have articulated a role for OFAT and showed they can be more effective than [[Fractional factorial design|fractional factorials]] under certain conditions (number of runs is limited, primary goal is to attain improvements in the system, and experimental error is not large compared to factor effects, which must be additive and independent of each other).<ref name=" Friedman, M., and Savage, L. J. (1947), “Planning Experiments Seeking Maxima,” in Techniques of Statistical Analysis, eds. C. Eisenhart, M. W. Hastay, and W. A. Wallis, New York: McGraw-Hill, pp. 365-372.

">Friedman, M., and Savage, L. J. (1947), “Planning Experiments Seeking Maxima,” in Techniques of Statistical Analysis, eds. C. Eisenhart, M. W. Hastay, and W. A. Wallis, New York: McGraw-Hill, pp. 365-372.</ref><ref name= "Cuthbert Daniel">Daniel , C. (1973) ,“One-at-a-Time Plans,” Journal of the American Statistical Association 68, 353-360</ref> Designed experiments remain nearly always preferred to OFAT with many types and methods available,<ref>See Category: Experimental design, at bottom.</ref> in addition to fractional factorials which, though usually requiring more runs than OFAT, do address the three concerns above.<ref name=Czitrom>[http://www.questia.com/googleScholar.qst?docId=5001888588 Czitrom (1999) "One-Factor-at-a-Time Versus Designed Experiments", American Statistician, 53, 2.]</ref> <!--formerly http://www.amstat.org/publications/tas/czitrom.pdf--> One modern design over which OFAT has no advantage in number of runs is the [[Plackett-Burman design|Plackett-Burman]] which, by having all factors vary simultaneously (an important quality in experimental designs),<ref>Ibid. name=Czitrom.< /ref> gives generally [[Efficiency (statistics)|greater precision in effect estimation]].

== References==

{{Ibid|date=July 2010}}

{{reflist}}